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    {
      "cell_type": "code",
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        "%matplotlib inline"
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      "source": [
        "\n# A demo of the mean-shift clustering algorithm\n\n\nReference:\n\nDorin Comaniciu and Peter Meer, \"Mean Shift: A robust approach toward\nfeature space analysis\". IEEE Transactions on Pattern Analysis and\nMachine Intelligence. 2002. pp. 603-619.\n\n\n"
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    {
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      "execution_count": null,
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      "source": [
        "print(__doc__)\n\nimport numpy as np\nfrom sklearn.cluster import MeanShift, estimate_bandwidth\nfrom sklearn.datasets.samples_generator import make_blobs\n\n# #############################################################################\n# Generate sample data\ncenters = [[1, 1], [-1, -1], [1, -1]]\nX, _ = make_blobs(n_samples=10000, centers=centers, cluster_std=0.6)\n\n# #############################################################################\n# Compute clustering with MeanShift\n\n# The following bandwidth can be automatically detected using\nbandwidth = estimate_bandwidth(X, quantile=0.2, n_samples=500)\n\nms = MeanShift(bandwidth=bandwidth, bin_seeding=True)\nms.fit(X)\nlabels = ms.labels_\ncluster_centers = ms.cluster_centers_\n\nlabels_unique = np.unique(labels)\nn_clusters_ = len(labels_unique)\n\nprint(\"number of estimated clusters : %d\" % n_clusters_)\n\n# #############################################################################\n# Plot result\nimport matplotlib.pyplot as plt\nfrom itertools import cycle\n\nplt.figure(1)\nplt.clf()\n\ncolors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')\nfor k, col in zip(range(n_clusters_), colors):\n    my_members = labels == k\n    cluster_center = cluster_centers[k]\n    plt.plot(X[my_members, 0], X[my_members, 1], col + '.')\n    plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,\n             markeredgecolor='k', markersize=14)\nplt.title('Estimated number of clusters: %d' % n_clusters_)\nplt.show()"
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